{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iFEmsVJI_2BR"
   },
   "source": [
    "# LiteLLM CometAPI Cookbook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "cBlUhCEP_xj4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Requirement already satisfied: fsspec>=2023.5.0 in /Users/xmx/.miniforge3/lib/python3.12/site-packages (from huggingface-hub<2.0,>=0.16.4->tokenizers->litellm) (2025.9.0)\n",
      "Requirement already satisfied: packaging>=20.9 in /Users/xmx/.miniforge3/lib/python3.12/site-packages (from huggingface-hub<2.0,>=0.16.4->tokenizers->litellm) (25.0)\n",
      "Requirement already satisfied: pyyaml>=5.1 in /Users/xmx/.miniforge3/lib/python3.12/site-packages (from huggingface-hub<2.0,>=0.16.4->tokenizers->litellm) (6.0.3)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in /Users/xmx/.miniforge3/lib/python3.12/site-packages (from requests>=2.26.0->tiktoken>=0.7.0->litellm) (3.4.0)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in /Users/xmx/.miniforge3/lib/python3.12/site-packages (from requests>=2.26.0->tiktoken>=0.7.0->litellm) (1.26.20)\n"
     ]
    }
   ],
   "source": [
    "!pip install litellm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Completion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "p-MQqWOT_1a7"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "os.environ['COMETAPI_KEY'] = \"Your_CometAPI_Key_Here\"\n",
    "api_key = os.getenv('COMETAPI_KEY')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Ze8JqMqWAARO",
    "outputId": "64f3e836-69fa-4f8e-fb35-088a913bbe98"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ModelResponse(id='msg_017L3DDDit8AkEgHRe2DQBc9', created=1760589916, model='claude-sonnet-4-5-20250929', object='chat.completion', system_fingerprint=None, choices=[Choices(finish_reason='stop', index=0, message=Message(content='I\\'ll create a simple Python script that says hi.\\n\\n<write_to_file>\\n<path>hello.py</path>\\n<content>#!/usr/bin/env python3\\n\"\"\"\\nA simple script that says hi!\\n\"\"\"\\n\\ndef say_hi(name=None):\\n    \"\"\"Say hi to someone, or just say hi generally.\"\"\"\\n    if name:\\n        print(f\"Hi, {name}!\")\\n    else:\\n        print(\"Hi!\")\\n\\nif __name__ == \"__main__\":\\n    # Say hi generally\\n    say_hi()\\n    \\n    # Say hi to someone specific\\n    say_hi(\"World\")\\n</content>\\n</write_to_file>\\n\\nI\\'ve created a simple Python script called `hello.py` that:\\n\\n1. Defines a `say_hi()` function that can optionally take a name parameter\\n2. Prints \"Hi!\" if no name is provided\\n3. Prints \"Hi, [name]!\" if a name is provided\\n4. Demonstrates both usages when run\\n\\nYou can run it with:\\n```bash\\npython hello.py\\n```\\n\\nThis will output:\\n```\\nHi!\\nHi, World!\\n```\\n\\nWould you like me to create versions in other programming languages, or modify this in any way?', role='assistant', tool_calls=None, function_call=None, provider_specific_fields=None), provider_specific_fields={})], usage=Usage(completion_tokens=290, prompt_tokens=26, total_tokens=316, completion_tokens_details=None, prompt_tokens_details=PromptTokensDetailsWrapper(audio_tokens=None, cached_tokens=None, text_tokens=None, image_tokens=None, cached_tokens_details={})))"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from litellm import completion\n",
    "response = completion(\n",
    "            model=\"cometapi/claude-sonnet-4-5-20250929\",\n",
    "            messages=[{\"role\": \"user\", \"content\": \"write code for saying hi\"}]\n",
    ")\n",
    "response"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-LnhELrnAM_J",
    "outputId": "d51c7ab7-d761-4bd1-f849-1534d9df4cd0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ModelResponse(id='chatcmpl-CRA9Uo6nsQ9C7kMJv1J4kyNDFJym7', created=1760589916, model='gpt-5-chat-latest', object='chat.completion', system_fingerprint='fp_2da73a467a', choices=[Choices(finish_reason='stop', index=0, message=Message(content='Sure! I can help you write a simple code that prints out \"Hi\" in different programming languages.  \\n\\nHere’s an example in **Python**:\\n\\n```python\\n# Simple Python program to say \"Hi\"\\nprint(\"Hi\")\\n```\\n\\nExample in **JavaScript**:\\n\\n```javascript\\n// Simple JavaScript program to say \"Hi\"\\nconsole.log(\"Hi\");\\n```\\n\\nExample in **C**:\\n\\n```c\\n#include <stdio.h>\\n\\nint main() {\\n    printf(\"Hi\\\\n\");\\n    return 0;\\n}\\n```\\n\\nExample in **Java**:\\n\\n```java\\npublic class SayHi {\\n    public static void main(String[] args) {\\n        System.out.println(\"Hi\");\\n    }\\n}\\n```\\n\\nWhich language would you like me to focus on, or do you want me to make it interactive so the program greets the user by name?', role='assistant', tool_calls=None, function_call=None, provider_specific_fields={'refusal': None}, annotations=[]), provider_specific_fields={})], usage=Usage(completion_tokens=174, prompt_tokens=12, total_tokens=186, completion_tokens_details=CompletionTokensDetailsWrapper(accepted_prediction_tokens=0, audio_tokens=0, reasoning_tokens=0, rejected_prediction_tokens=0, text_tokens=None), prompt_tokens_details=PromptTokensDetailsWrapper(audio_tokens=0, cached_tokens=0, text_tokens=None, image_tokens=None)))"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "response = completion(\n",
    "            model=\"cometapi/gpt-5-chat-latest\",\n",
    "            messages=[{\"role\": \"user\", \"content\": \"write code for saying hi\"}]\n",
    ")\n",
    "response"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "dJBOUYdwCEn1",
    "outputId": "ffa18679-ec15-4dad-fe2b-68665cdf36b0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ModelResponse(id='02176058998406949c23b3bf3d52941de23f13565a086747738f0', created=1760589991, model='deepseek-v3.2-exp', object='chat.completion', system_fingerprint=None, choices=[Choices(finish_reason='stop', index=0, message=Message(content='Here are several ways to say \"hi\" in different programming languages:\\n\\n## Python\\n```python\\nprint(\"Hi!\")\\n```\\n\\n## JavaScript (Browser)\\n```javascript\\nconsole.log(\"Hi!\");\\n// or\\nalert(\"Hi!\");\\n```\\n\\n## JavaScript (Node.js)\\n```javascript\\nconsole.log(\"Hi!\");\\n```\\n\\n## Java\\n```java\\npublic class Hello {\\n    public static void main(String[] args) {\\n        System.out.println(\"Hi!\");\\n    }\\n}\\n```\\n\\n## C\\n```c\\n#include <stdio.h>\\n\\nint main() {\\n    printf(\"Hi!\\\\n\");\\n    return 0;\\n}\\n```\\n\\n## C++\\n```cpp\\n#include <iostream>\\n\\nint main() {\\n    std::cout << \"Hi!\" << std::endl;\\n    return 0;\\n}\\n```\\n\\n## C#\\n```csharp\\nusing System;\\n\\nclass Program {\\n    static void Main() {\\n        Console.WriteLine(\"Hi!\");\\n    }\\n}\\n```\\n\\n## PHP\\n```php\\n<?php\\necho \"Hi!\";\\n?>\\n```\\n\\n## Ruby\\n```ruby\\nputs \"Hi!\"\\n```\\n\\n## Go\\n```go\\npackage main\\n\\nimport \"fmt\"\\n\\nfunc main() {\\n    fmt.Println(\"Hi!\")\\n}\\n```\\n\\n## Rust\\n```rust\\nfn main() {\\n    println!(\"Hi!\");\\n}\\n```\\n\\n## Swift\\n```swift\\nprint(\"Hi!\")\\n```\\n\\n## Kotlin\\n```kotlin\\nfun main() {\\n    println(\"Hi!\")\\n}\\n```\\n\\n## HTML (webpage)\\n```html\\n<!DOCTYPE html>\\n<html>\\n<head>\\n    <title>Hi Page</title>\\n</head>\\n<body>\\n    <h1>Hi!</h1>\\n</body>\\n</html>\\n```\\n\\nThe Python version is probably the simplest if you\\'re just getting started!', role='assistant', tool_calls=None, function_call=None, provider_specific_fields={'refusal': None}), provider_specific_fields={})], usage=Usage(completion_tokens=347, prompt_tokens=10, total_tokens=357, completion_tokens_details=CompletionTokensDetailsWrapper(accepted_prediction_tokens=None, audio_tokens=None, reasoning_tokens=0, rejected_prediction_tokens=None, text_tokens=None), prompt_tokens_details=PromptTokensDetailsWrapper(audio_tokens=None, cached_tokens=0, text_tokens=None, image_tokens=None)), service_tier='default')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "response = completion(\n",
    "            model=\"cometapi/deepseek-v3.2-exp\",\n",
    "            messages=[{\"role\": \"user\", \"content\": \"write code for saying hi\"}]\n",
    ")\n",
    "response"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Streaming"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Streaming Responses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I'm\n",
      " doing\n",
      " well\n",
      " —\n",
      " thanks\n",
      " for\n",
      " asking\n",
      "!\n",
      " How\n",
      " can\n",
      " I\n",
      " help\n",
      " you\n",
      " today\n",
      "?\n",
      "\n"
     ]
    }
   ],
   "source": [
    "messages = [{\"role\": \"user\", \"content\": \"Hey, how's it going?\"}]\n",
    "response = completion(model=\"cometapi/gpt-5-mini\", messages=messages, stream=True)\n",
    "for part in response:\n",
    "    print(part.choices[0].delta.content or \"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Async Completion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ModelResponse(id='chatcmpl-CRAAkmfczlmCEnCM55D9CKexbRenn', created=1760589994, model='gpt-5-mini-2025-08-07', object='chat.completion', system_fingerprint=None, choices=[Choices(finish_reason='stop', index=0, message=Message(content=\"I'm doing well, thanks — how are you? How can I help today?\", role='assistant', tool_calls=None, function_call=None, provider_specific_fields={'refusal': None}, annotations=[]), provider_specific_fields={'content_filter_results': {'hate': {'filtered': False, 'severity': 'safe'}, 'protected_material_code': {'filtered': False, 'detected': False}, 'protected_material_text': {'filtered': False, 'detected': False}, 'self_harm': {'filtered': False, 'severity': 'safe'}, 'sexual': {'filtered': False, 'severity': 'safe'}, 'violence': {'filtered': False, 'severity': 'safe'}}})], usage=Usage(completion_tokens=26, prompt_tokens=12, total_tokens=38, completion_tokens_details=CompletionTokensDetailsWrapper(accepted_prediction_tokens=0, audio_tokens=0, reasoning_tokens=0, rejected_prediction_tokens=0, text_tokens=None), prompt_tokens_details=PromptTokensDetailsWrapper(audio_tokens=0, cached_tokens=0, text_tokens=None, image_tokens=None)), prompt_filter_results=[{'prompt_index': 0, 'content_filter_results': {'hate': {'filtered': False, 'severity': 'safe'}, 'jailbreak': {'filtered': False, 'detected': False}, 'self_harm': {'filtered': False, 'severity': 'safe'}, 'sexual': {'filtered': False, 'severity': 'safe'}, 'violence': {'filtered': False, 'severity': 'safe'}}}])\n"
     ]
    }
   ],
   "source": [
    "from litellm import acompletion\n",
    "import asyncio\n",
    "\n",
    "async def test_get_response():\n",
    "    user_message = \"Hello, how are you?\"\n",
    "    messages = [{\"content\": user_message, \"role\": \"user\"}]\n",
    "    response = await acompletion(model=\"cometapi/gpt-5-mini\", messages=messages)\n",
    "    return response\n",
    "\n",
    "response = await test_get_response()\n",
    "print(response)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Async Streaming"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test acompletion + streaming\n",
      "response: <litellm.litellm_core_utils.streaming_handler.CustomStreamWrapper object at 0x11fa532f0>\n",
      "ModelResponseStream(id='chatcmpl-CRAAl9VMDBB5skZt638Qx86K9h1Hb', created=1760589996, model='gpt-5-mini', object='chat.completion.chunk', system_fingerprint=None, choices=[StreamingChoices(finish_reason=None, index=0, delta=Delta(provider_specific_fields=None, content='Hi', role='assistant', function_call=None, tool_calls=None, audio=None), logprobs=None)], provider_specific_fields=None, citations=None)\n",
      "ModelResponseStream(id='chatcmpl-CRAAl9VMDBB5skZt638Qx86K9h1Hb', created=1760589996, model='gpt-5-mini', object='chat.completion.chunk', system_fingerprint=None, choices=[StreamingChoices(finish_reason=None, index=0, delta=Delta(provider_specific_fields=None, content=' —', role=None, function_call=None, tool_calls=None, audio=None), logprobs=None)], provider_specific_fields=None, citations=None)\n",
      "ModelResponseStream(id='chatcmpl-CRAAl9VMDBB5skZt638Qx86K9h1Hb', created=1760589996, model='gpt-5-mini', object='chat.completion.chunk', system_fingerprint=None, choices=[StreamingChoices(finish_reason=None, index=0, delta=Delta(provider_specific_fields=None, content=' I', role=None, function_call=None, tool_calls=None, audio=None), logprobs=None)], provider_specific_fields=None, citations=None)\n",
      "ModelResponseStream(id='chatcmpl-CRAAl9VMDBB5skZt638Qx86K9h1Hb', created=1760589996, model='gpt-5-mini', object='chat.completion.chunk', system_fingerprint=None, choices=[StreamingChoices(finish_reason=None, index=0, delta=Delta(provider_specific_fields=None, content='’m', role=None, function_call=None, tool_calls=None, audio=None), logprobs=None)], provider_specific_fields=None, citations=None)\n",
      "ModelResponseStream(id='chatcmpl-CRAAl9VMDBB5skZt638Qx86K9h1Hb', created=1760589996, model='gpt-5-mini', object='chat.completion.chunk', system_fingerprint=None, choices=[StreamingChoices(finish_reason=None, index=0, delta=Delta(provider_specific_fields=None, content=' doing', role=None, function_call=None, tool_calls=None, audio=None), logprobs=None)], provider_specific_fields=None, citations=None)\n",
      "ModelResponseStream(id='chatcmpl-CRAAl9VMDBB5skZt638Qx86K9h1Hb', created=1760589996, model='gpt-5-mini', object='chat.completion.chunk', system_fingerprint=None, choices=[StreamingChoices(finish_reason=None, index=0, delta=Delta(provider_specific_fields=None, content=' well', role=None, function_call=None, tool_calls=None, audio=None), logprobs=None)], provider_specific_fields=None, citations=None)\n",
      "ModelResponseStream(id='chatcmpl-CRAAl9VMDBB5skZt638Qx86K9h1Hb', created=1760589996, model='gpt-5-mini', object='chat.completion.chunk', system_fingerprint=None, choices=[StreamingChoices(finish_reason=None, index=0, delta=Delta(provider_specific_fields=None, content=',', role=None, function_call=None, tool_calls=None, audio=None), logprobs=None)], provider_specific_fields=None, citations=None)\n",
      "ModelResponseStream(id='chatcmpl-CRAAl9VMDBB5skZt638Qx86K9h1Hb', created=1760589996, model='gpt-5-mini', object='chat.completion.chunk', system_fingerprint=None, choices=[StreamingChoices(finish_reason=None, index=0, delta=Delta(provider_specific_fields=None, content=' thanks', role=None, function_call=None, tool_calls=None, audio=None), logprobs=None)], provider_specific_fields=None, citations=None)\n",
      "ModelResponseStream(id='chatcmpl-CRAAl9VMDBB5skZt638Qx86K9h1Hb', created=1760589996, model='gpt-5-mini', object='chat.completion.chunk', system_fingerprint=None, choices=[StreamingChoices(finish_reason=None, index=0, delta=Delta(provider_specific_fields=None, content='!', role=None, function_call=None, tool_calls=None, audio=None), logprobs=None)], provider_specific_fields=None, citations=None)\n",
      "ModelResponseStream(id='chatcmpl-CRAAl9VMDBB5skZt638Qx86K9h1Hb', created=1760589996, model='gpt-5-mini', object='chat.completion.chunk', system_fingerprint=None, choices=[StreamingChoices(finish_reason=None, index=0, delta=Delta(provider_specific_fields=None, content=' How', role=None, function_call=None, tool_calls=None, audio=None), logprobs=None)], provider_specific_fields=None, citations=None)\n",
      "ModelResponseStream(id='chatcmpl-CRAAl9VMDBB5skZt638Qx86K9h1Hb', created=1760589996, model='gpt-5-mini', object='chat.completion.chunk', system_fingerprint=None, choices=[StreamingChoices(finish_reason=None, index=0, delta=Delta(provider_specific_fields=None, content=' are', role=None, function_call=None, tool_calls=None, audio=None), logprobs=None)], provider_specific_fields=None, citations=None)\n",
      "ModelResponseStream(id='chatcmpl-CRAAl9VMDBB5skZt638Qx86K9h1Hb', created=1760589996, model='gpt-5-mini', object='chat.completion.chunk', system_fingerprint=None, choices=[StreamingChoices(finish_reason=None, index=0, delta=Delta(provider_specific_fields=None, content=' you', role=None, function_call=None, tool_calls=None, audio=None), logprobs=None)], provider_specific_fields=None, citations=None)\n",
      "ModelResponseStream(id='chatcmpl-CRAAl9VMDBB5skZt638Qx86K9h1Hb', created=1760589996, model='gpt-5-mini', object='chat.completion.chunk', system_fingerprint=None, choices=[StreamingChoices(finish_reason=None, index=0, delta=Delta(provider_specific_fields=None, content='?', role=None, function_call=None, tool_calls=None, audio=None), logprobs=None)], provider_specific_fields=None, citations=None)\n",
      "ModelResponseStream(id='chatcmpl-CRAAl9VMDBB5skZt638Qx86K9h1Hb', created=1760589996, model='gpt-5-mini', object='chat.completion.chunk', system_fingerprint=None, choices=[StreamingChoices(finish_reason=None, index=0, delta=Delta(provider_specific_fields=None, content=' What', role=None, function_call=None, tool_calls=None, audio=None), logprobs=None)], provider_specific_fields=None, citations=None)\n",
      "ModelResponseStream(id='chatcmpl-CRAAl9VMDBB5skZt638Qx86K9h1Hb', created=1760589996, model='gpt-5-mini', object='chat.completion.chunk', system_fingerprint=None, choices=[StreamingChoices(finish_reason=None, index=0, delta=Delta(provider_specific_fields=None, content=' can', role=None, function_call=None, tool_calls=None, audio=None), logprobs=None)], provider_specific_fields=None, citations=None)\n",
      "ModelResponseStream(id='chatcmpl-CRAAl9VMDBB5skZt638Qx86K9h1Hb', created=1760589996, model='gpt-5-mini', object='chat.completion.chunk', system_fingerprint=None, choices=[StreamingChoices(finish_reason=None, index=0, delta=Delta(provider_specific_fields=None, content=' I', role=None, function_call=None, tool_calls=None, audio=None), logprobs=None)], provider_specific_fields=None, citations=None)\n",
      "ModelResponseStream(id='chatcmpl-CRAAl9VMDBB5skZt638Qx86K9h1Hb', created=1760589996, model='gpt-5-mini', object='chat.completion.chunk', system_fingerprint=None, choices=[StreamingChoices(finish_reason=None, index=0, delta=Delta(provider_specific_fields=None, content=' help', role=None, function_call=None, tool_calls=None, audio=None), logprobs=None)], provider_specific_fields=None, citations=None)\n",
      "ModelResponseStream(id='chatcmpl-CRAAl9VMDBB5skZt638Qx86K9h1Hb', created=1760589996, model='gpt-5-mini', object='chat.completion.chunk', system_fingerprint=None, choices=[StreamingChoices(finish_reason=None, index=0, delta=Delta(provider_specific_fields=None, content=' you', role=None, function_call=None, tool_calls=None, audio=None), logprobs=None)], provider_specific_fields=None, citations=None)\n",
      "ModelResponseStream(id='chatcmpl-CRAAl9VMDBB5skZt638Qx86K9h1Hb', created=1760589996, model='gpt-5-mini', object='chat.completion.chunk', system_fingerprint=None, choices=[StreamingChoices(finish_reason=None, index=0, delta=Delta(provider_specific_fields=None, content=' with', role=None, function_call=None, tool_calls=None, audio=None), logprobs=None)], provider_specific_fields=None, citations=None)\n",
      "ModelResponseStream(id='chatcmpl-CRAAl9VMDBB5skZt638Qx86K9h1Hb', created=1760589996, model='gpt-5-mini', object='chat.completion.chunk', system_fingerprint=None, choices=[StreamingChoices(finish_reason=None, index=0, delta=Delta(provider_specific_fields=None, content=' today', role=None, function_call=None, tool_calls=None, audio=None), logprobs=None)], provider_specific_fields=None, citations=None)\n",
      "ModelResponseStream(id='chatcmpl-CRAAl9VMDBB5skZt638Qx86K9h1Hb', created=1760589996, model='gpt-5-mini', object='chat.completion.chunk', system_fingerprint=None, choices=[StreamingChoices(finish_reason=None, index=0, delta=Delta(provider_specific_fields=None, content='?', role=None, function_call=None, tool_calls=None, audio=None), logprobs=None)], provider_specific_fields=None, citations=None)\n",
      "ModelResponseStream(id='chatcmpl-CRAAl9VMDBB5skZt638Qx86K9h1Hb', created=1760589996, model='gpt-5-mini', object='chat.completion.chunk', system_fingerprint=None, choices=[StreamingChoices(finish_reason='stop', index=0, delta=Delta(provider_specific_fields=None, content=None, role=None, function_call=None, tool_calls=None, audio=None), logprobs=None)], provider_specific_fields=None)\n"
     ]
    }
   ],
   "source": [
    "from litellm import acompletion\n",
    "import asyncio, os, traceback\n",
    "\n",
    "async def completion_call():\n",
    "    try:\n",
    "        print(\"test acompletion + streaming\")\n",
    "        response = await acompletion(\n",
    "            model=\"cometapi/gpt-5-mini\", \n",
    "            messages=[{\"content\": \"Hello, how are you?\", \"role\": \"user\"}], \n",
    "            stream=True\n",
    "        )\n",
    "        print(f\"response: {response}\")\n",
    "        async for chunk in response:\n",
    "            print(chunk)\n",
    "    except:\n",
    "        print(f\"error occurred: {traceback.format_exc()}\")\n",
    "        pass\n",
    "\n",
    "await completion_call()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Embedding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EmbeddingResponse(model='text-embedding-3-small', data=[{'object': 'embedding', 'index': 0, 'embedding': [-0.018048199, 0.0047550877, -0.013976435, -0.021936804, -0.038773336, -0.03708264, 0.03854791, -0.0007172257, 0.026473511, -0.0027438616, -0.019823432, -0.011947598, -0.013426959, -0.0059914105, 0.020485623, 0.04269012, -0.028276922, -0.015216281, -0.03325039, 0.045057096, 0.0037477135, 0.015793936, -0.005188329, -0.0071713766, 0.008446445, 0.0070938864, 0.0027632343, 0.025656339, 0.022091785, -0.026797561, -0.029840818, -0.0542714, 0.017907308, -0.03454659, -0.014582269, 0.0429719, 0.03575826, 0.007939235, -0.010123054, -0.029587213, 0.018921727, 0.022556728, 0.019598005, 0.008819807, 0.01655475, 0.043310042, -0.034321167, 0.004441604, 0.032686826, 0.047226828, -0.0043253684, 0.006681779, 0.008995921, 0.06593721, 0.0066078105, -0.023190739, -0.00777721, 0.049875587, -0.028023317, -0.019386668, -0.0013252605, 0.009214303, -0.0055828253, -0.007432026, -0.01199691, 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-0.018414518, -0.023303451, -0.025797231, -0.009524264]}], object='list', usage=Usage(completion_tokens=0, prompt_tokens=3, total_tokens=3, completion_tokens_details=None, prompt_tokens_details=None))\n"
     ]
    }
   ],
   "source": [
    "import litellm\n",
    "\n",
    "\n",
    "async def main():\n",
    "    response = await litellm.aembedding(\n",
    "        model=\"cometapi/text-embedding-3-small\", # The model name must include prefix \"openai\" + the model name from ai/ml api\n",
    "        api_key=api_key,  # your aiml api-key\n",
    "        api_base=\"https://api.cometapi.com/v1\", # 👈 the URL has changed from v2 to v1\n",
    "        input=\"Your text string\",\n",
    "    )\n",
    "    print(response)\n",
    "\n",
    "await main()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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-0.018414518, -0.023303451, -0.025797231, -0.009524264]}], object='list', usage=Usage(completion_tokens=0, prompt_tokens=3, total_tokens=3, completion_tokens_details=None, prompt_tokens_details=None))\n"
     ]
    }
   ],
   "source": [
    "import litellm\n",
    "\n",
    "\n",
    "async def main():\n",
    "    response = await litellm.aembedding(\n",
    "        model=\"cometapi/text-embedding-3-small\", # The model name must include prefix \"cometapi/\" + the model name from CometAPI\n",
    "        api_key=api_key,  # your CometAPI api-key\n",
    "        api_base=\"https://api.cometapi.com/v1\",\n",
    "        input=\"Your text string\",\n",
    "    )\n",
    "    print(response)\n",
    "\n",
    "\n",
    "await main()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Async Image Generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ImageResponse(created=1760591151, background=None, data=[ImageObject(b64_json=None, revised_prompt=\"Generate an image of an adorable baby sea otter. It should be floating on its back in a calm, clear ocean, playfully grasping a colorful shell in its small paws. The sun is setting in the background, casting a peaceful orange and purple hue across the sky and reflecting upon the ocean waves. The otter's fur is a deep, rich brown and appears silky and wet, with glints of sunlight catching on it. Its eyes are bright, expressing joy and curiosity as it examines its newfound treasure.\", url='https://oaidalleapiprodscus.blob.core.windows.net/private/org-OKnsK88id12jfvnKByup1O0l/user-3GxuMyEg9YMU8LFCPHi31prf/img-7PUEF8Wb6thGDAuZWLJjSnfP.png?st=2025-10-16T04%3A05%3A51Z&se=2025-10-16T06%3A05%3A51Z&sp=r&sv=2024-08-04&sr=b&rscd=inline&rsct=image/png&skoid=38e27a3b-6174-4d3e-90ac-d7d9ad49543f&sktid=a48cca56-e6da-484e-a814-9c849652bcb3&skt=2025-10-16T02%3A51%3A01Z&ske=2025-10-17T02%3A51%3A01Z&sks=b&skv=2024-08-04&sig=IZKG2VE%2B6VdOe5Tq0Zk/5bVyGK/oK/yO8g%2BDX4krpug%3D')], output_format=None, quality=None, size=None, usage=Usage(completion_tokens=0, prompt_tokens=0, total_tokens=0, completion_tokens_details=None, prompt_tokens_details=None, input_tokens=0, input_tokens_details={'image_tokens': 0, 'text_tokens': 0}, output_tokens=0))\n"
     ]
    }
   ],
   "source": [
    "import asyncio\n",
    "\n",
    "import litellm\n",
    "\n",
    "\n",
    "async def main():\n",
    "    response = await litellm.aimage_generation(\n",
    "        model=\"cometapi/dall-e-3\",  # The model name must include prefix \"cometapi/\" + the model name from CometAPI\n",
    "        api_key=api_key,  # your cometapi api-key\n",
    "        api_base=\"https://api.cometapi.com/v1\",\n",
    "        prompt=\"A cute baby sea otter\",\n",
    "    )\n",
    "    print(response)\n",
    "\n",
    "\n",
    "await main()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
